Joint Analysis Methods Research Articles

Inversion of reclaimed soil water content based on a combination of multi-attributes of ground penetrating radar signals

Rapid, accurate, and non-destructive acquisition of the distribution of reclaimed soil moisture information can provide data for the rapid monitoring of reclaimed soil in areas experiencing coal mining subsidence. However, the water content inversion methods based on ground penetrating radar (GPR) are mostly single-attribute analysis methods, which are easily affected by the soil structure. This paper proposes a multi-attribute joint analysis method, which can reduce the influence of complex soil structure on the prediction results. Surveys and soil sampling using GPR were performed on a subsided reclamation area in Huaibei City, Anhui Province, China. Correlation analysis of GPR attribute information and volumetric water content (VWC) showed that frequency peak (FP), average envelope amplitude (AEA), energy, instantaneous amplitude area, instantaneous frequency area, and average instantaneous frequency values were significantly related to the VWC of reclaimed soil. The applicability of different single-attribute analysis methods under the condition of reclaimed soil was compared. Results showed that the predictive effects of FP and AEA attributes were better than other attributes. Generally, the single-attribute analysis method was greatly affected by the structure of reclaimed soil, so the accuracy and reliability of this method need to be further optimized. The prediction results of the single- and multi-attribute joint analysis methods in the structure of reclaimed soil were compared and analyzed. This showed that the prediction accuracy and model reliability of the multi-attribute method are both higher than those of the single-attribute method. The multi-attribute method can overcome the problem of insufficient accuracy of water content detection of ground-seeking radar in soil with a complex structure. Finally, the multi-attribute method was used to obtain the water distribution information in the reclamation area. Results of this study provide new methods and ideas for the prediction of water content by GPR in soils with complex structure.

A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data

The analysis of hierarchical interactions has long been a challenging problem due to the large number of candidate main effects and interaction effects, and the need for accommodating the “main effects, interactions” hierarchy. The two-stage analysis methods enjoy simplicity and low computational cost, but contradict the fact that the outcome of interest is attributable to the joint effects of multiple main factors and their interactions. The existing joint analysis methods can accurately describe the underlying data generating process, but suffer from prohibitively high computational cost. And it is not straightforward to extend their optimization algorithms to general loss functions. To address this need, we develop a new computational method that is much faster than the existing joint analysis methods and rivals the runtimes of two-stage analysis. The proposed method, HierFabs , adopts the framework of the forward and backward stagewise algorithm and enjoys computational efficiency and broad applicability. To accommodate hierarchy without imposing additional constraints, it has newly developed forward and backward steps. It naturally accommodates the strong and weak hierarchy, and makes optimization much simpler and faster than in the existing studies. Optimality of HierFabs sequences is investigated theoretically. Simulations show that it outperforms the existing methods. The analysis of TCGA data on melanoma demonstrates its competitive practical performance. Supplementary materials for this article are available online.

Early detection of colorectal cancer based on circular DNA and common clinical detection indicators.

BACKGROUNDColorectal cancer (CRC) is the third most common cancer worldwide, and it is the second leading cause of death from cancer in the world, accounting for approximately 9% of all cancer deaths. Early detection of CRC is urgently needed in clinical practice.AIMTo build a multi-parameter diagnostic model for early detection of CRC. METHODSTotal 59 colorectal polyps (CRP) groups, and 101 CRC patients (38 early-stage CRC and 63 advanced CRC) for model establishment. In addition, 30 CRP groups, and 62 CRC patients (30 early-stage CRC and 32 advanced CRC) were separately included to validate the model. 51 commonly used clinical detection indicators and the 4 extrachromosomal circular DNA markers NDUFB7, CAMK1D, PIK3CD and PSEN2 that we screened earlier. Four multi-parameter joint analysis methods: binary logistic regression analysis, discriminant analysis, classification tree and neural network to establish a multi-parameter joint diagnosis model. RESULTSNeural network included carcinoembryonic antigen (CEA), ischemia-modified albumin (IMA), sialic acid (SA), PIK3CD and lipoprotein a (LPa) was chosen as the optimal multi-parameter combined auxiliary diagnosis model to distinguish CRP and CRC group, when it differentiated 59 CRP and 101 CRC, its overall accuracy was 90.8%, its area under the curve (AUC) was 0.959 (0.934, 0.985), and the sensitivity and specificity were 91.5% and 82.2%, respectively. After validation, when distinguishing based on 30 CRP and 62 CRC patients, the AUC was 0.965 (0.930-1.000), and its sensitivity and specificity were 66.1% and 70.0%. When distinguishing based on 30 CRP and 32 early-stage CRC patients, the AUC was 0.960 (0.916-1.000), with a sensitivity and specificity of 87.5% and 90.0%, distinguishing based on 30 CRP and 30 advanced CRC patients, the AUC was 0.970 (0.936-1.000), with a sensitivity and specificity of 96.7% and 86.7%.CONCLUSIONWe built a multi-parameter neural network diagnostic model included CEA, IMA, SA, PIK3CD and LPa for early detection of CRC, compared to the conventional CEA, it showed significant improvement.

Complex Space Monitoring Data Analysis to Determine Environmental Trends of Poland-Ukraine Border Areas

Abstract A comprehensive approach to assessing the current ecological status of the Poland-Ukraine border areas has been considered, identifying possible negative effects associated with river water pollution. The main environmental impact factors that are directly related to the negative effects of anthropogenic load on the changing ecological status of the border areas and adjacent terrain have been identified. Particular attention was paid to the pollution monitoring data analysis of the Ukrainian part of the Western Bug basin. Using the space monitoring results, pollution sources that were not recorded in the official reports were identified. Their negative impact on the change of quality of life within the border areas was assessed. The integration specificities of heterogeneous time-differentiated ecosystem monitoring data were considered. The integration expediency of interdisciplinary joint analysis methods of decryption data and field observations for making operational decisions to prevent negative consequences of possible changes in the ecological status is shown. Modelling was utilized to determine the estimated contamination time of the river systems of Western Bug and Vistula from the Ukraine territory. The tendency of further changes in the ecological status of the analyzed areas has been ascertained, including the assessment of possible negative consequences.

Identification of gene-environment interactions with marginal penalization.

Gene-environment (G-E) interaction analysis has been extensively conducted for complex diseases. In marginal analysis, the common practice is to conduct likelihood-based (and other "standard") estimation with each marginal model, and then select significant G-E interactions and main effects based on p values and multiple comparisons adjustment. One limitation of this approach is that the identification results often do not respect the "main effects, interactions" hierarchy, which has been stressed in recent G-E interaction analyses. There is some recent effort tackling this problem, however, with very complex formulations. Another limitation of the common practice is that it may not perform well when regularization is needed, for example, because of "non-normal" distributions. In this article, we propose a marginal penalization approach which adopts a novel penalty to directly tackle the aforementioned problems. The proposed approach has a framework more coherent with that of the recently developed joint analysis methods and an intuitive formulation, and can be effectively realized. In simulation, it outperforms the popular significance-based analysis and simple penalization-based alternatives. Promising findings are made in the analysis of a single-nucleotide polymorphism and a gene expression data.

Experimental evaluation of sand particle identification in oil–water–gas multiphase flows based on vibration signal analysis

Sand–oil–water–gas pipe flow is common in the chemical and petroleum industries. In this paper, the identification of the solid phase in sand–oil–water–gas bubbly flow was investigated using a vibration-sensing method. Four identical high-frequency vibration sensors were selected to obtain the multiphase flow signals around the exit of a pipe elbow. Typical frequency-domain, mutual coherence, time-domain statistical, and time-domain joint analysis methods were applied for sand-vibration signal identification and characterization. Verification experiments were performed, and good agreement was found between the sand concentration (0.05–0.25wt.%), particle size (96–180μm) and vibrational energy in water–gas bubbly flow and oil–water–gas bubbly flow. Sand characteristic frequency bands of 14–15.39 and 15.63–16.09kHz for water–gas bubbly flow and of 14–16.45 and 19.31–20kHz for oil–water–gas bubbly flow were found. Sand particles were more easily identified by varying the water velocity than the gas velocity. The validity of the detected sand signals was also verified using an acoustic sensor. It was proven that the vibration-sensing method can be used to characterize solid particles in oil–water–gas bubbly multiphase flows, laying the foundation for solid identification in more complex multiphase flows.

Identifying gene-gene interactions using penalized tensor regression.

Gene-gene (G×G) interactions have been shown to be critical for the fundamental mechanisms and development of complex diseases beyond main genetic effects. The commonly adopted marginal analysis is limited by considering only a small number of G factors at a time. With the "main effects, interactions" hierarchical constraint, many of the existing joint analysis methods suffer from prohibitively high computational cost. In this study, we propose a new method for identifying important G×G interactions under joint modeling. The proposed method adopts tensor regression to accommodate high data dimensionality and the penalization technique for selection. It naturally accommodates the strong hierarchical structure without imposing additional constraints, making optimization much simpler and faster than in the existing studies. It outperforms multiple alternatives in simulation. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer and melanoma demonstrates that it can identify markers with important implications and better prediction performance.

Similarity of markers identified from cancer gene expression studies: observations from GEO

Gene expression profiling has been extensively conducted in cancer research. The analysis of multiple independent cancer gene expression datasets may provide additional information and complement single-dataset analysis. In this study, we conduct multi-dataset analysis and are interested in evaluating the similarity of cancer-associated genes identified from different datasets. The first objective of this study is to briefly review some statistical methods that can be used for such evaluation. Both marginal analysis and joint analysis methods are reviewed. The second objective is to apply those methods to 26 Gene Expression Omnibus (GEO) datasets on five types of cancers. Our analysis suggests that for the same cancer, the marker identification results may vary significantly across datasets, and different datasets share few common genes. In addition, datasets on different cancers share few common genes. The shared genetic basis of datasets on the same or different cancers, which has been suggested in the literature, is not observed in the analysis of GEO data.

Heat Transfer and Thermal Load Analysis of Exhaust Manifold

With the appearance of high temperature resistance alloy steel and the requirement of light weight for vehicles, more and more exhaust manifolds are made from newly developed alloy steel. The changing of material directly affects the design and manufacturing process. To estimate the thermal load of the tight-coupled exhaust manifold, the joint analysis methods of CFD and FEM are put forward to predict the temperature distribution, thermal stress and deformation. Using Computational Fluid Dynamics (CFD) method, gas temperature and convective heat transfer coefficient, adjoining the inside and outside surfaces of the exhaust manifold, are estimated firstly in this paper. Then these results are mapped to the finite element mesh of exhaust manifold, which are created for the heat transfer analysis. At last, thermal stress and thermal deformation analysis are presented by taking nonlinear material properties into account, which provide some reference to evaluate the cooling capacity and structure design of exhaust manifold.

A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism.

BackgroundGenome-wide Association Studies (GWAS) have proved invaluable for the identification of disease susceptibility genes. However, the prioritization of candidate genes and regions for follow-up studies often proves difficult due to false-positive associations caused by statistical noise and multiple-testing. In order to address this issue, we propose the novel GWAS noise reduction (GWAS-NR) method as a way to increase the power to detect true associations in GWAS, particularly in complex diseases such as autism.MethodsGWAS-NR utilizes a linear filter to identify genomic regions demonstrating correlation among association signals in multiple datasets. We used computer simulations to assess the ability of GWAS-NR to detect association against the commonly used joint analysis and Fisher's methods. Furthermore, we applied GWAS-NR to a family-based autism GWAS of 597 families and a second existing autism GWAS of 696 families from the Autism Genetic Resource Exchange (AGRE) to arrive at a compendium of autism candidate genes. These genes were manually annotated and classified by a literature review and functional grouping in order to reveal biological pathways which might contribute to autism aetiology.ResultsComputer simulations indicate that GWAS-NR achieves a significantly higher classification rate for true positive association signals than either the joint analysis or Fisher's methods and that it can also achieve this when there is imperfect marker overlap across datasets or when the closest disease-related polymorphism is not directly typed. In two autism datasets, GWAS-NR analysis resulted in 1535 significant linkage disequilibrium (LD) blocks overlapping 431 unique reference sequencing (RefSeq) genes. Moreover, we identified the nearest RefSeq gene to the non-gene overlapping LD blocks, producing a final candidate set of 860 genes. Functional categorization of these implicated genes indicates that a significant proportion of them cooperate in a coherent pathway that regulates the directional protrusion of axons and dendrites to their appropriate synaptic targets.ConclusionsAs statistical noise is likely to particularly affect studies of complex disorders, where genetic heterogeneity or interaction between genes may confound the ability to detect association, GWAS-NR offers a powerful method for prioritizing regions for follow-up studies. Applying this method to autism datasets, GWAS-NR analysis indicates that a large subset of genes involved in the outgrowth and guidance of axons and dendrites is implicated in the aetiology of autism.

Joint analysis of the data of optical and radar sensors: Potentialities, limitations, and prospects

Stages of the joint processing of the data of remote sensing of the Earth surface in the optical and radar bands are presented. Application capabilities of the Earth observation from space in two above bands are compared. Potentialities and limitations of joint analysis methods are illustrated. Most preferable parameters of the configuration of the layout of promising integrated remote sensing of the Earth from space in the radio and optical bands in the interests of the economical activity on the territory of the Russian Federation are determined.

A joint search for gravitational wave bursts with AURIGA and LIGO

The first simultaneous operation of the AURIGA detectorhttp://www.auriga.lnl.infn.it and the LIGO observatoryhttp://www.ligo.org was an opportunity to explore real data, joint analysis methods between two very different types of gravitational wave detectors: resonant bars and interferometers. This paper describes a coincident gravitational wave burst search, where data from the LIGO interferometers are cross-correlated at the time of AURIGA candidate events to identify coincident transients. The analysis pipeline is tuned with two thresholds, on the signal-to-noise ratio of AURIGA candidate events and on the significance of the cross-correlation test in LIGO. The false alarm rate is estimated by introducing time shifts between data sets and the network detection efficiency is measured by adding simulated gravitational wave signals to the detector output. The simulated waveforms have a significant fraction of power in the narrower AURIGA band. In the absence of a detection, we discuss how to set an upper limit on the rate of gravitational waves and to interpret it according to different source models. Due to the short amount of analyzed data and to the high rate of non-Gaussian transients in the detectors' noise at the time, the relevance of this study is methodological: this was the first joint search for gravitational wave bursts among detectors with such different spectral sensitivity and the first opportunity for the resonant and interferometric communities to unify languages and techniques in the pursuit of their common goal.